Emotion Detection Using CNN in Python Using Keras
By AYUSH KESARWANI
In this article, We’ll Learn Real-Time Emotion Detection Using CNN. The Dataset we will use is already present in Kaggle you can download the dataset from here – Dataset-FER2013, It contains 48*48 px grayscale images. There are 7 classes/categories in this dataset (0=angry, 1=disgust, 2=fear, 3=happy, 4=sad, 5=surprise, 6=neutral).
Note: Use Google Colab and enable GPU
Since we are using google colab. Upload the dataset in your google drive, we’ll access the dataset from there.
IMPORT REQUIRED LIBRARIES
Here we are going to use the Keras deep learning API that comes with the TensorFlow module of Python for this emotion detection task. There are some other modules we are going to use. So below we are importing all the required Python modules.
import sys,os import tensorflow as tf import numpy as np import pandas as pd import keras from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D, BatchNormalization,AveragePooling2D from keras.losses import categorical_crossentropy from keras.optimizers import Adam from keras.regularizers import l2 from keras.utils import np_utils
LOADING, READING AND PRE-PROCESSING THE DATASET
See the Python code below:
from google.colab import drive
drive.mount('/content/drive') #this will ask an authentication code of your drive, after giving access to your drive you are ready to read the dataset from there
os.chdir("ENTER PATH WHERE DATASET IS IN YOUR DRIVE") # EXAMPLE:- /content/drive/My Drive/Emotion Detection
df = pd.read_csv("archive.zip") #READING DATASET
print(df)
OUTPUT:-
emotion pixels Usage 0 0 70 80 82 72 58 58 60 63 54 58 60 48 89 115 121... Training 1 0 151 150 147 155 148 133 111 140 170 174 182 15... Training 2 2 231 212 156 164 174 138 161 173 182 200 106 38... Training 3 4 24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1... Training 4 6 4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84... Training ... ... ... ... 35882 6 50 36 17 22 23 29 33 39 34 37 37 37 39 43 48 5... PrivateTest 35883 3 178 174 172 173 181 188 191 194 196 199 200 20... PrivateTest 35884 0 17 17 16 23 28 22 19 17 25 26 20 24 31 19 27 9... PrivateTest 35885 3 30 28 28 29 31 30 42 68 79 81 77 67 67 71 63 6... PrivateTest 35886 2 19 13 14 12 13 16 21 33 50 57 71 84 97 108 122... PrivateTest [35887 rows x 3 columns]
Now, we’ll create different lists of the train and test image pixels. And Check in Usage, if found Training then adds pixels in X_train and respective emotions in train_y. Similarly, for pixels of the Public test, we add it to test lists. Therefore, Code:-
X_train,train_y,X_test,test_y=[],[],[],[]
for index, row in df.iterrows():
val=row['pixels'].split(" ")
if 'Training' in row['Usage']:
X_train.append(np.array(val,'float32'))
train_y.append(row['emotion'])
elif 'PublicTest' in row['Usage']:
X_test.append(np.array(val,'float32'))
test_y.append(row['emotion'])
Then converting and reshaping the pixels of X_train, X_test, and Converting emotions in train_y, test_y to categorical.
num_features = 64 num_labels = 7 batch_size = 64 epochs = 175 width, height = 48, 48 X_train = np.array(X_train,'float32') train_y = np.array(train_y,'float32') X_test = np.array(X_test,'float32') test_y = np.array(test_y,'float32') train_y=np_utils.to_categorical(train_y, num_classes=num_labels) test_y=np_utils.to_categorical(test_y, num_classes=num_labels) ˆ X_train -= np.mean(X_train, axis=0) X_train /= np.std(X_train, axis=0) X_test -= np.mean(X_test, axis=0) X_test /= np.std(X_test, axis=0) X_train = X_train.reshape(X_train.shape[0], 48, 48, 1) X_test = X_test.reshape(X_test.shape[0], 48, 48, 1)
BUILDING MODEL
model = Sequential() model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', input_shape=(X_train.shape[1:]), padding='same')) model.add(Conv2D(64,kernel_size= (3, 3), activation='relu', padding='same')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Dropout(0.3)) #2nd convolution layer model.add(Conv2D(64, (3, 3), activation='relu', padding='same')) model.add(Conv2D(64, (3, 3), activation='relu', padding='same')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Dropout(0.3)) #3rd convolution layer model.add(Conv2D(128, (3, 3), activation='relu', padding='same')) model.add(Conv2D(128, (3, 3), activation='relu', padding='same')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Flatten()) model.add(Dense(num_labels, activation='softmax')) model.summary()
OUTPUT:-
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d (Conv2D) (None, 48, 48, 64) 640 _________________________________________________________________ conv2d_1 (Conv2D) (None, 48, 48, 64) 36928 _________________________________________________________________ batch_normalization (BatchNo (None, 48, 48, 64) 256 _________________________________________________________________ max_pooling2d (MaxPooling2D) (None, 24, 24, 64) 0 _________________________________________________________________ dropout (Dropout) (None, 24, 24, 64) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 24, 24, 64) 36928 _________________________________________________________________ conv2d_3 (Conv2D) (None, 24, 24, 64) 36928 _________________________________________________________________ batch_normalization_1 (Batch (None, 24, 24, 64) 256 _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 12, 12, 64) 0 _________________________________________________________________ dropout_1 (Dropout) (None, 12, 12, 64) 0 _________________________________________________________________ conv2d_4 (Conv2D) (None, 12, 12, 128) 73856 _________________________________________________________________ conv2d_5 (Conv2D) (None, 12, 12, 128) 147584 _________________________________________________________________ batch_normalization_2 (Batch (None, 12, 12, 128) 512 _________________________________________________________________ max_pooling2d_2 (MaxPooling2 (None, 6, 6, 128) 0 _________________________________________________________________ flatten (Flatten) (None, 4608) 0 _________________________________________________________________ dense (Dense) (None, 7) 32263 ================================================================= Total params: 366,151 Trainable params: 365,639 Non-trainable params: 512 _________________________________________________________________
COMPILE AND TRAIN THE MODEL
model.compile(loss=categorical_crossentropy,
optimizer=Adam(),
metrics=['accuracy'])
model.fit(X_train, train_y,
steps_per_epoch=len(X_train) / batch_size,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(X_test, test_y)
)
OUTPUT:-
Epoch 1/175 449/448 [==============================] - 21s 47ms/step - loss: 1.8567 - accuracy: 0.3392 - val_loss: 1.7904 - val_accuracy: 0.3413 Epoch 2/175 449/448 [==============================] - 20s 46ms/step - loss: 1.5181 - accuracy: 0.4424 - val_loss: 1.4303 - val_accuracy: 0.4921 Epoch 3/175 449/448 [==============================] - 20s 45ms/step - loss: 1.3464 - accuracy: 0.5004 - val_loss: 1.3037 - val_accuracy: 0.5127 Epoch 4/175 449/448 [==============================] - 21s 46ms/step - loss: 1.2346 - accuracy: 0.5389 - val_loss: 1.3261 - val_accuracy: 0.5088 Epoch 5/175 449/448 [==============================] - 20s 46ms/step - loss: 1.1319 - accuracy: 0.5797 - val_loss: 1.2096 - val_accuracy: 0.5570 Epoch 6/175 449/448 [==============================] - 21s 46ms/step - loss: 1.0737 - accuracy: 0.6008 - val_loss: 1.1866 - val_accuracy: 0.5770 Epoch 7/175 449/448 [==============================] - 21s 46ms/step - loss: 0.9985 - accuracy: 0.6278 - val_loss: 1.1647 - val_accuracy: 0.5717 Epoch 8/175 449/448 [==============================] - 21s 46ms/step - loss: 0.9328 - accuracy: 0.6566 - val_loss: 1.1829 - val_accuracy: 0.5723 Epoch 9/175 449/448 [==============================] - 20s 46ms/step - loss: 0.8712 - accuracy: 0.6794 - val_loss: 1.2172 - val_accuracy: 0.5740 Epoch 10/175 449/448 [==============================] - 20s 46ms/step - loss: 0.8068 - accuracy: 0.7029 - val_loss: 1.1768 - val_accuracy: 0.5926 Epoch 11/175 449/448 [==============================] - 20s 46ms/step - loss: 0.7480 - accuracy: 0.7270 - val_loss: 1.2087 - val_accuracy: 0.5904 Epoch 12/175 449/448 [==============================] - 20s 46ms/step - loss: 0.6874 - accuracy: 0.7489 - val_loss: 1.2136 - val_accuracy: 0.5965 Epoch 13/175 449/448 [==============================] - 21s 46ms/step - loss: 0.6417 - accuracy: 0.7655 - val_loss: 1.2881 - val_accuracy: 0.5901 Epoch 14/175 449/448 [==============================] - 21s 46ms/step - loss: 0.5789 - accuracy: 0.7911 - val_loss: 1.3005 - val_accuracy: 0.6024 Epoch 15/175 449/448 [==============================] - 21s 46ms/step - loss: 0.5451 - accuracy: 0.8023 - val_loss: 1.3627 - val_accuracy: 0.5834 Epoch 16/175 449/448 [==============================] - 20s 46ms/step - loss: 0.4888 - accuracy: 0.8208 - val_loss: 1.3465 - val_accuracy: 0.6049 Epoch 17/175 449/448 [==============================] - 20s 46ms/step - loss: 0.4543 - accuracy: 0.8348 - val_loss: 1.3999 - val_accuracy: 0.6038 Epoch 18/175 449/448 [==============================] - 20s 45ms/step - loss: 0.4209 - accuracy: 0.8485 - val_loss: 1.4820 - val_accuracy: 0.5954 Epoch 19/175 449/448 [==============================] - 21s 46ms/step - loss: 0.3861 - accuracy: 0.8607 - val_loss: 1.6021 - val_accuracy: 0.5890 Epoch 20/175 449/448 [==============================] - 21s 46ms/step - loss: 0.3689 - accuracy: 0.8664 - val_loss: 1.5178 - val_accuracy: 0.5993 Epoch 21/175 449/448 [==============================] - 21s 46ms/step - loss: 0.3422 - accuracy: 0.8773 - val_loss: 1.5716 - val_accuracy: 0.5890 Epoch 22/175 449/448 [==============================] - 21s 46ms/step - loss: 0.3305 - accuracy: 0.8799 - val_loss: 1.6905 - val_accuracy: 0.5899 Epoch 23/175 449/448 [==============================] - 21s 46ms/step - loss: 0.3136 - accuracy: 0.8859 - val_loss: 1.5996 - val_accuracy: 0.6052 Epoch 24/175 449/448 [==============================] - 20s 46ms/step - loss: 0.2951 - accuracy: 0.8936 - val_loss: 1.7175 - val_accuracy: 0.5924 Epoch 25/175 449/448 [==============================] - 21s 46ms/step - loss: 0.2752 - accuracy: 0.9032 - val_loss: 1.6579 - val_accuracy: 0.6004 Epoch 26/175 449/448 [==============================] - 20s 46ms/step - loss: 0.2607 - accuracy: 0.9065 - val_loss: 1.7728 - val_accuracy: 0.5982 Epoch 27/175 449/448 [==============================] - 20s 46ms/step - loss: 0.2575 - accuracy: 0.9065 - val_loss: 1.8015 - val_accuracy: 0.5865 Epoch 28/175 449/448 [==============================] - 20s 45ms/step - loss: 0.2491 - accuracy: 0.9107 - val_loss: 1.7949 - val_accuracy: 0.5918 Epoch 29/175 449/448 [==============================] - 20s 46ms/step - loss: 0.2291 - accuracy: 0.9203 - val_loss: 1.7892 - val_accuracy: 0.5979 Epoch 30/175 449/448 [==============================] - 20s 46ms/step - loss: 0.2292 - accuracy: 0.9172 - val_loss: 1.8955 - val_accuracy: 0.6049 Epoch 31/175 449/448 [==============================] - 21s 46ms/step - loss: 0.2275 - accuracy: 0.9182 - val_loss: 1.9020 - val_accuracy: 0.5910 Epoch 32/175 449/448 [==============================] - 21s 46ms/step - loss: 0.2151 - accuracy: 0.9241 - val_loss: 1.8902 - val_accuracy: 0.6030 Epoch 33/175 449/448 [==============================] - 21s 46ms/step - loss: 0.2083 - accuracy: 0.9259 - val_loss: 1.8927 - val_accuracy: 0.5977 Epoch 34/175 449/448 [==============================] - 21s 46ms/step - loss: 0.2085 - accuracy: 0.9269 - val_loss: 1.8889 - val_accuracy: 0.6041 Epoch 35/175 449/448 [==============================] - 20s 46ms/step - loss: 0.2022 - accuracy: 0.9290 - val_loss: 1.9135 - val_accuracy: 0.6004 Epoch 36/175 449/448 [==============================] - 20s 45ms/step - loss: 0.1883 - accuracy: 0.9340 - val_loss: 1.9541 - val_accuracy: 0.6060 Epoch 37/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1945 - accuracy: 0.9317 - val_loss: 2.0019 - val_accuracy: 0.6043 Epoch 38/175 449/448 [==============================] - 20s 46ms/step - loss: 0.1883 - accuracy: 0.9328 - val_loss: 1.9417 - val_accuracy: 0.5974 Epoch 39/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1741 - accuracy: 0.9405 - val_loss: 1.9762 - val_accuracy: 0.5974 Epoch 40/175 449/448 [==============================] - 20s 46ms/step - loss: 0.1821 - accuracy: 0.9370 - val_loss: 2.1234 - val_accuracy: 0.5901 Epoch 41/175 449/448 [==============================] - 20s 45ms/step - loss: 0.1657 - accuracy: 0.9421 - val_loss: 2.0580 - val_accuracy: 0.5921 Epoch 42/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1768 - accuracy: 0.9378 - val_loss: 2.1744 - val_accuracy: 0.5865 Epoch 43/175 449/448 [==============================] - 20s 46ms/step - loss: 0.1611 - accuracy: 0.9440 - val_loss: 2.0404 - val_accuracy: 0.6018 Epoch 44/175 449/448 [==============================] - 20s 46ms/step - loss: 0.1720 - accuracy: 0.9402 - val_loss: 2.1080 - val_accuracy: 0.6071 Epoch 45/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1598 - accuracy: 0.9452 - val_loss: 2.0619 - val_accuracy: 0.5926 Epoch 46/175 449/448 [==============================] - 20s 46ms/step - loss: 0.1523 - accuracy: 0.9466 - val_loss: 2.0883 - val_accuracy: 0.5968 Epoch 47/175 449/448 [==============================] - 20s 46ms/step - loss: 0.1521 - accuracy: 0.9476 - val_loss: 2.1487 - val_accuracy: 0.5924 Epoch 48/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1515 - accuracy: 0.9484 - val_loss: 2.1605 - val_accuracy: 0.5874 Epoch 49/175 449/448 [==============================] - 20s 46ms/step - loss: 0.1503 - accuracy: 0.9479 - val_loss: 2.2089 - val_accuracy: 0.6002 Epoch 50/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1464 - accuracy: 0.9508 - val_loss: 2.1147 - val_accuracy: 0.5979 Epoch 51/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1418 - accuracy: 0.9510 - val_loss: 2.2313 - val_accuracy: 0.5815 Epoch 52/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1470 - accuracy: 0.9504 - val_loss: 2.1788 - val_accuracy: 0.5999 Epoch 53/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1338 - accuracy: 0.9542 - val_loss: 2.1339 - val_accuracy: 0.6024 Epoch 54/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1360 - accuracy: 0.9540 - val_loss: 2.2155 - val_accuracy: 0.6032 Epoch 55/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1302 - accuracy: 0.9560 - val_loss: 2.2032 - val_accuracy: 0.5932 Epoch 56/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1372 - accuracy: 0.9528 - val_loss: 2.2022 - val_accuracy: 0.5890 Epoch 57/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1346 - accuracy: 0.9541 - val_loss: 2.1667 - val_accuracy: 0.6021 Epoch 58/175 449/448 [==============================] - 20s 46ms/step - loss: 0.1314 - accuracy: 0.9550 - val_loss: 2.2446 - val_accuracy: 0.6007 Epoch 59/175 449/448 [==============================] - 20s 46ms/step - loss: 0.1298 - accuracy: 0.9560 - val_loss: 2.2614 - val_accuracy: 0.5915 Epoch 60/175 449/448 [==============================] - 20s 46ms/step - loss: 0.1226 - accuracy: 0.9585 - val_loss: 2.2066 - val_accuracy: 0.5890 Epoch 61/175 449/448 [==============================] - 20s 46ms/step - loss: 0.1236 - accuracy: 0.9583 - val_loss: 2.2095 - val_accuracy: 0.5971 Epoch 62/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1218 - accuracy: 0.9607 - val_loss: 2.2142 - val_accuracy: 0.6038 Epoch 63/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1197 - accuracy: 0.9594 - val_loss: 2.2424 - val_accuracy: 0.5991 Epoch 64/175 449/448 [==============================] - 20s 46ms/step - loss: 0.1091 - accuracy: 0.9628 - val_loss: 2.2398 - val_accuracy: 0.5940 Epoch 65/175 449/448 [==============================] - 20s 46ms/step - loss: 0.1179 - accuracy: 0.9608 - val_loss: 2.3311 - val_accuracy: 0.5798 Epoch 66/175 449/448 [==============================] - 20s 46ms/step - loss: 0.1151 - accuracy: 0.9624 - val_loss: 2.2754 - val_accuracy: 0.5890 Epoch 67/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1264 - accuracy: 0.9585 - val_loss: 2.2798 - val_accuracy: 0.5918 Epoch 68/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1119 - accuracy: 0.9634 - val_loss: 2.3058 - val_accuracy: 0.5882 Epoch 69/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1113 - accuracy: 0.9635 - val_loss: 2.3107 - val_accuracy: 0.5940 Epoch 70/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1106 - accuracy: 0.9646 - val_loss: 2.2894 - val_accuracy: 0.5949 Epoch 71/175 449/448 [==============================] - 20s 46ms/step - loss: 0.1072 - accuracy: 0.9640 - val_loss: 2.3135 - val_accuracy: 0.5940 Epoch 72/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1116 - accuracy: 0.9625 - val_loss: 2.3210 - val_accuracy: 0.5940 Epoch 73/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1056 - accuracy: 0.9659 - val_loss: 2.2949 - val_accuracy: 0.5982 Epoch 74/175 449/448 [==============================] - 20s 46ms/step - loss: 0.1021 - accuracy: 0.9659 - val_loss: 2.3719 - val_accuracy: 0.5968 Epoch 75/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1037 - accuracy: 0.9659 - val_loss: 2.3883 - val_accuracy: 0.5963 Epoch 76/175 449/448 [==============================] - 20s 46ms/step - loss: 0.1080 - accuracy: 0.9638 - val_loss: 2.3795 - val_accuracy: 0.5899 Epoch 77/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1062 - accuracy: 0.9649 - val_loss: 2.3508 - val_accuracy: 0.5910 Epoch 78/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0955 - accuracy: 0.9689 - val_loss: 2.3829 - val_accuracy: 0.5949 Epoch 79/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0987 - accuracy: 0.9672 - val_loss: 2.4050 - val_accuracy: 0.5826 Epoch 80/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1007 - accuracy: 0.9677 - val_loss: 2.3465 - val_accuracy: 0.5952 Epoch 81/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0976 - accuracy: 0.9687 - val_loss: 2.3163 - val_accuracy: 0.5963 Epoch 82/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1010 - accuracy: 0.9681 - val_loss: 2.3415 - val_accuracy: 0.5882 Epoch 83/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0964 - accuracy: 0.9682 - val_loss: 2.4223 - val_accuracy: 0.5865 Epoch 84/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0922 - accuracy: 0.9701 - val_loss: 2.4284 - val_accuracy: 0.6041 Epoch 85/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0973 - accuracy: 0.9681 - val_loss: 2.3537 - val_accuracy: 0.6041 Epoch 86/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0914 - accuracy: 0.9706 - val_loss: 2.3283 - val_accuracy: 0.6038 Epoch 87/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0881 - accuracy: 0.9712 - val_loss: 2.3613 - val_accuracy: 0.5954 Epoch 88/175 449/448 [==============================] - 21s 46ms/step - loss: 0.1014 - accuracy: 0.9678 - val_loss: 2.4222 - val_accuracy: 0.5901 Epoch 89/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0882 - accuracy: 0.9722 - val_loss: 2.3874 - val_accuracy: 0.5977 Epoch 90/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0875 - accuracy: 0.9715 - val_loss: 2.4285 - val_accuracy: 0.5949 Epoch 91/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0880 - accuracy: 0.9724 - val_loss: 2.3932 - val_accuracy: 0.5988 Epoch 92/175 449/448 [==============================] - 20s 46ms/step - loss: 0.0895 - accuracy: 0.9716 - val_loss: 2.4383 - val_accuracy: 0.5954 Epoch 93/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0919 - accuracy: 0.9692 - val_loss: 2.3882 - val_accuracy: 0.5993 Epoch 94/175 449/448 [==============================] - 20s 46ms/step - loss: 0.0868 - accuracy: 0.9722 - val_loss: 2.4348 - val_accuracy: 0.5968 Epoch 95/175 449/448 [==============================] - 20s 46ms/step - loss: 0.0862 - accuracy: 0.9733 - val_loss: 2.4100 - val_accuracy: 0.6013 Epoch 96/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0869 - accuracy: 0.9722 - val_loss: 2.3346 - val_accuracy: 0.6013 Epoch 97/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0874 - accuracy: 0.9723 - val_loss: 2.3942 - val_accuracy: 0.6110 Epoch 98/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0841 - accuracy: 0.9725 - val_loss: 2.3957 - val_accuracy: 0.5915 Epoch 99/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0902 - accuracy: 0.9703 - val_loss: 2.3519 - val_accuracy: 0.6035 Epoch 100/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0833 - accuracy: 0.9738 - val_loss: 2.3758 - val_accuracy: 0.6063 Epoch 101/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0768 - accuracy: 0.9758 - val_loss: 2.4069 - val_accuracy: 0.6055 Epoch 102/175 449/448 [==============================] - 20s 46ms/step - loss: 0.0827 - accuracy: 0.9741 - val_loss: 2.4630 - val_accuracy: 0.5993 Epoch 103/175 449/448 [==============================] - 20s 45ms/step - loss: 0.0805 - accuracy: 0.9744 - val_loss: 2.5062 - val_accuracy: 0.6055 Epoch 104/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0763 - accuracy: 0.9765 - val_loss: 2.5178 - val_accuracy: 0.5938 Epoch 105/175 449/448 [==============================] - 20s 46ms/step - loss: 0.0800 - accuracy: 0.9758 - val_loss: 2.4403 - val_accuracy: 0.6052 Epoch 106/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0806 - accuracy: 0.9739 - val_loss: 2.4415 - val_accuracy: 0.5965 Epoch 107/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0758 - accuracy: 0.9763 - val_loss: 2.3893 - val_accuracy: 0.5985 Epoch 108/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0811 - accuracy: 0.9739 - val_loss: 2.4333 - val_accuracy: 0.5991 Epoch 109/175 449/448 [==============================] - 20s 46ms/step - loss: 0.0767 - accuracy: 0.9759 - val_loss: 2.4260 - val_accuracy: 0.5910 Epoch 110/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0752 - accuracy: 0.9751 - val_loss: 2.4478 - val_accuracy: 0.5910 Epoch 111/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0760 - accuracy: 0.9761 - val_loss: 2.4458 - val_accuracy: 0.5952 Epoch 112/175 449/448 [==============================] - 21s 47ms/step - loss: 0.0770 - accuracy: 0.9757 - val_loss: 2.4142 - val_accuracy: 0.5963 Epoch 113/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0760 - accuracy: 0.9760 - val_loss: 2.4680 - val_accuracy: 0.5843 Epoch 114/175 449/448 [==============================] - 20s 46ms/step - loss: 0.0765 - accuracy: 0.9751 - val_loss: 2.4400 - val_accuracy: 0.5991 Epoch 115/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0749 - accuracy: 0.9770 - val_loss: 2.5477 - val_accuracy: 0.5921 Epoch 116/175 449/448 [==============================] - 20s 46ms/step - loss: 0.0699 - accuracy: 0.9780 - val_loss: 2.4813 - val_accuracy: 0.5993 Epoch 117/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0739 - accuracy: 0.9780 - val_loss: 2.5207 - val_accuracy: 0.5988 Epoch 118/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0753 - accuracy: 0.9760 - val_loss: 2.4967 - val_accuracy: 0.5918 Epoch 119/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0767 - accuracy: 0.9762 - val_loss: 2.4891 - val_accuracy: 0.5921 Epoch 120/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0708 - accuracy: 0.9772 - val_loss: 2.5227 - val_accuracy: 0.5991 Epoch 121/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0702 - accuracy: 0.9786 - val_loss: 2.4075 - val_accuracy: 0.6007 Epoch 122/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0723 - accuracy: 0.9766 - val_loss: 2.4571 - val_accuracy: 0.6004 Epoch 123/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0724 - accuracy: 0.9773 - val_loss: 2.5567 - val_accuracy: 0.5960 Epoch 124/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0675 - accuracy: 0.9789 - val_loss: 2.4151 - val_accuracy: 0.6082 Epoch 125/175 449/448 [==============================] - 20s 46ms/step - loss: 0.0692 - accuracy: 0.9784 - val_loss: 2.4698 - val_accuracy: 0.6013 Epoch 126/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0727 - accuracy: 0.9782 - val_loss: 2.4374 - val_accuracy: 0.6032 Epoch 127/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0667 - accuracy: 0.9803 - val_loss: 2.4918 - val_accuracy: 0.5999 Epoch 128/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0684 - accuracy: 0.9800 - val_loss: 2.5180 - val_accuracy: 0.5963 Epoch 129/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0715 - accuracy: 0.9778 - val_loss: 2.4824 - val_accuracy: 0.6057 Epoch 130/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0702 - accuracy: 0.9785 - val_loss: 2.4656 - val_accuracy: 0.5963 Epoch 131/175 449/448 [==============================] - 20s 46ms/step - loss: 0.0632 - accuracy: 0.9814 - val_loss: 2.5017 - val_accuracy: 0.6116 Epoch 132/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0692 - accuracy: 0.9784 - val_loss: 2.4782 - val_accuracy: 0.5982 Epoch 133/175 449/448 [==============================] - 20s 46ms/step - loss: 0.0661 - accuracy: 0.9800 - val_loss: 2.5537 - val_accuracy: 0.6018 Epoch 134/175 449/448 [==============================] - 20s 46ms/step - loss: 0.0658 - accuracy: 0.9794 - val_loss: 2.5170 - val_accuracy: 0.6004 Epoch 135/175 449/448 [==============================] - 20s 46ms/step - loss: 0.0631 - accuracy: 0.9809 - val_loss: 2.5300 - val_accuracy: 0.6016 Epoch 136/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0664 - accuracy: 0.9790 - val_loss: 2.5197 - val_accuracy: 0.5968 Epoch 137/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0653 - accuracy: 0.9793 - val_loss: 2.5825 - val_accuracy: 0.5915 Epoch 138/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0670 - accuracy: 0.9788 - val_loss: 2.4759 - val_accuracy: 0.6060 Epoch 139/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0685 - accuracy: 0.9796 - val_loss: 2.5147 - val_accuracy: 0.5985 Epoch 140/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0603 - accuracy: 0.9820 - val_loss: 2.5310 - val_accuracy: 0.5952 Epoch 141/175 449/448 [==============================] - 20s 46ms/step - loss: 0.0656 - accuracy: 0.9797 - val_loss: 2.5063 - val_accuracy: 0.6035 Epoch 142/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0694 - accuracy: 0.9783 - val_loss: 2.4645 - val_accuracy: 0.6046 Epoch 143/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0671 - accuracy: 0.9801 - val_loss: 2.4294 - val_accuracy: 0.5991 Epoch 144/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0610 - accuracy: 0.9822 - val_loss: 2.5173 - val_accuracy: 0.5977 Epoch 145/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0598 - accuracy: 0.9806 - val_loss: 2.4935 - val_accuracy: 0.5979 Epoch 146/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0579 - accuracy: 0.9821 - val_loss: 2.5222 - val_accuracy: 0.6041 Epoch 147/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0634 - accuracy: 0.9815 - val_loss: 2.5418 - val_accuracy: 0.5977 Epoch 148/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0600 - accuracy: 0.9822 - val_loss: 2.5331 - val_accuracy: 0.6043 Epoch 149/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0594 - accuracy: 0.9824 - val_loss: 2.5022 - val_accuracy: 0.5991 Epoch 150/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0617 - accuracy: 0.9816 - val_loss: 2.5150 - val_accuracy: 0.6080 Epoch 151/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0591 - accuracy: 0.9823 - val_loss: 2.5346 - val_accuracy: 0.5999 Epoch 152/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0661 - accuracy: 0.9801 - val_loss: 2.4523 - val_accuracy: 0.6021 Epoch 153/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0583 - accuracy: 0.9813 - val_loss: 2.5202 - val_accuracy: 0.6032 Epoch 154/175 449/448 [==============================] - 20s 46ms/step - loss: 0.0614 - accuracy: 0.9821 - val_loss: 2.5766 - val_accuracy: 0.6018 Epoch 155/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0573 - accuracy: 0.9832 - val_loss: 2.4998 - val_accuracy: 0.5988 Epoch 156/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0572 - accuracy: 0.9835 - val_loss: 2.5666 - val_accuracy: 0.6057 Epoch 157/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0613 - accuracy: 0.9823 - val_loss: 2.4897 - val_accuracy: 0.6010 Epoch 158/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0582 - accuracy: 0.9838 - val_loss: 2.5121 - val_accuracy: 0.6041 Epoch 159/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0624 - accuracy: 0.9811 - val_loss: 2.4837 - val_accuracy: 0.6069 Epoch 160/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0585 - accuracy: 0.9827 - val_loss: 2.6001 - val_accuracy: 0.6102 Epoch 161/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0590 - accuracy: 0.9823 - val_loss: 2.5550 - val_accuracy: 0.6066 Epoch 162/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0581 - accuracy: 0.9826 - val_loss: 2.5223 - val_accuracy: 0.6007 Epoch 163/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0589 - accuracy: 0.9826 - val_loss: 2.5876 - val_accuracy: 0.5991 Epoch 164/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0530 - accuracy: 0.9836 - val_loss: 2.5902 - val_accuracy: 0.6010 Epoch 165/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0575 - accuracy: 0.9840 - val_loss: 2.5464 - val_accuracy: 0.6030 Epoch 166/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0563 - accuracy: 0.9840 - val_loss: 2.5730 - val_accuracy: 0.6030 Epoch 167/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0589 - accuracy: 0.9829 - val_loss: 2.4697 - val_accuracy: 0.6027 Epoch 168/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0553 - accuracy: 0.9842 - val_loss: 2.5325 - val_accuracy: 0.5993 Epoch 169/175 449/448 [==============================] - 20s 46ms/step - loss: 0.0577 - accuracy: 0.9838 - val_loss: 2.6346 - val_accuracy: 0.5996 Epoch 170/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0550 - accuracy: 0.9845 - val_loss: 2.5300 - val_accuracy: 0.6063 Epoch 171/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0528 - accuracy: 0.9850 - val_loss: 2.5102 - val_accuracy: 0.6071 Epoch 172/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0583 - accuracy: 0.9825 - val_loss: 2.5085 - val_accuracy: 0.6027 Epoch 173/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0561 - accuracy: 0.9840 - val_loss: 2.5149 - val_accuracy: 0.6071 Epoch 174/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0588 - accuracy: 0.9822 - val_loss: 2.4584 - val_accuracy: 0.6066 Epoch 175/175 449/448 [==============================] - 21s 46ms/step - loss: 0.0479 - accuracy: 0.9857 - val_loss: 2.5053 - val_accuracy: 0.6069 <tensorflow.python.keras.callbacks.History at 0x7f643d069828>
We have successfully trained our model and got 98% accuracy.
Save model
m_json = model.to_json()
with open("m.json", "w") as json_file:
json_file.write(m_json)
model.save_weights("Detection_Emotion.h5")
Now, comes the interesting part. We’ll use Opencv to predict emotions in Real-Time. But first of all, download the saved weights and model. We’ll work on our local machines now. why?? Because VideoCapture doesn’t work in google colab, in other words, using a webcam in google colab isn’t easy.
NOTE:- YOU NEED TENSORFLOW VERSION ‘2.3.0’ AND KERAS VERSION ‘2.4.1’ IN YOUR LOCAL MACHINE.
#import libraries
import os
import cv2
import numpy as np
from keras.models import model_from_json
from keras.preprocessing import image
from tensorflow.keras.models import load_model
#loading model
model = model_from_json(open("m.json", "r").read())
#loading weights
model.load_weights('Detection_Emotion.h5')
Then, use the code given below to open your webcam and predict emotions.
face_haar_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
cap=cv2.VideoCapture(0)
while True:
ret,test_img=cap.read()
if not ret:
continue
gray_img= cv2.cvtColor(test_img, cv2.COLOR_BGR2GRAY)
faces_detected = face_haar_cascade.detectMultiScale(gray_img, 1.32, 5)
for (x,y,w,h) in faces_detected:
cv2.rectangle(test_img,(x,y),(x+w,y+h),(255,0,0),thickness=7)
roi_gray=gray_img[y:y+w,x:x+h]
roi_gray=cv2.resize(roi_gray,(48,48))
img_pixels = image.img_to_array(roi_gray)
img_pixels = np.expand_dims(img_pixels, axis = 0)
img_pixels /= 255
predictions = model.predict(img_pixels)
max_index = np.argmax(predictions[0])
emotions = ('angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral')
predicted_emotion = emotions[max_index]
cv2.putText(test_img, predicted_emotion, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)
resized_img = cv2.resize(test_img, (1000, 700))
cv2.imshow('Facial emotion analysis ',resized_img)
if cv2.waitKey(10) == ord('q'):#press 'q' key to exit
break
cap.release()
cv2.destroyAllWindows
OUTPUT:-
Here, you can observe our model is predicting well. We have Successfully Done Real-Time Emotion Detection.
Thanks for Reading.
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Great job… Informative and good explanation..
Nice work bro…Informative learned something new